Args:

categorical_column: A CategoricalColumn created by a
categorical_column_with_* function. This column produces the sparse IDs
that are inputs to the embedding lookup.

dimension: An integer specifying dimension of the embedding, must be > 0.

combiner: A string specifying how to reduce if there are multiple entries in
a single row. Currently 'mean', 'sqrtn' and 'sum' are supported, with
'mean' the default. 'sqrtn' often achieves good accuracy, in particular
with bag-of-words columns. Each of this can be thought as example level
normalizations on the column. For more information, see
tf.embedding_lookup_sparse.

initializer: A variable initializer function to be used in embedding
variable initialization. If not specified, defaults to
truncated_normal_initializer with mean 0.0 and
standard deviation 1/sqrt(dimension).

ckpt_to_load_from: String representing checkpoint name/pattern from which to
restore column weights. Required if tensor_name_in_ckpt is not None.

tensor_name_in_ckpt: Name of the Tensor in ckpt_to_load_from from which
to restore the column weights. Required if ckpt_to_load_from is not
None.

max_norm: If not None, embedding values are l2-normalized to this value.

trainable: Whether or not the embedding is trainable. Default is True.

Returns:

DenseColumn that converts from sparse input.

Raises:

ValueError: if dimension not > 0.

ValueError: if exactly one of ckpt_to_load_from and tensor_name_in_ckpt
is specified.